Real-time BigData and Predictive Analytical Architecture for healthcare application

In the digital era, data is one of the most important assets since it conceals valuable information. Developers of data-intensive systems have new challenges at each level of streaming, storing, and processing large quantities of data in a variety of forms and speeds. Obtaining useful information at the proper time and place is also crucial. Since the value of information is inversely proportional to time, real-time data processing and analytics are receiving more attention. Due to the importance of real-time data processing and analytics, this study focuses on real-time data processing concepts and terminology, popular technologies used in real-time data processing and analytics, popular NoSQL storage technologies used in real-time data processing, and real-time data processing application areas. The purpose of this paper is to provide researchers of real-time analysis and developers of data-intensive systems with a comparative perspective on real-time data processing by highlighting the key characteristics of real-time data processing technologies, NoSQL storage technologies, their application domains, and selected examples from previous studies.

Real-time big data processing is a crucial need of meteorology today. As a matter of fact, the weather is a determining factor in decision making in different areas such as air or sea transport, so acquiring knowledge of the environment in real time is a consequential question. A promising approach for big data treatment in real time is based on the concept of complex events processing CEP). The CEP aims to identify significant events in a cloud of simple and individually detectable events in real time. The meteorological variables, constituting simple event's spaces, offer significant flow of data to be processed using CEP technologies. This real-time data processing is done by phase of their integration with weather systems. This paper discusses the big data's meteorological aspects, and shows the architectural model for integration of weather data and its processing in real time.

The problems of design, computer simulation, manufacture of the device sample and its characteristics investigation of the acoustooptical scanner based on surface acoustic waves for 2D data processing in real time are discussed. The method is based on waveguide Bragg and radiation acoustooptic interactions with a synthesized optical aperture using a few optical beams. The total information field is divided into subareas, each of which is irradiated using an individual optical beam controlled by the corresponding waveguide acoustooptic Bragg cell and interaction cell. During the work elaboration of unit contraction, computer simulation, fabrication sample on based Y-cut lithium niobate and its characteristics investigated. This unit makes it possible to process in real time 2600times650 scanning points (information bits) representing two-dimensional arrays

Purpose: The study sought to explore the impact of edge computing on real-time data processing. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the impact of edge computing on real-time data processing. Preliminary empirical review reveled that edge computing significantly reduced latency and enhanced efficiency in real-time data processing across various industries by bringing computational resources closer to data sources. It highlighted the technology's ability to handle large volumes of IoT-generated data, improve security by localizing data processing, and drive innovation and economic growth through new applications and services. Edge computing's decentralized approach proved essential for reliable and robust data handling, particularly in critical sectors like healthcare and finance, ultimately solidifying its importance in the digital transformation landscape. Unique Contribution to Theory, Practice and Policy: The Diffusion of Innovations Theory, Resource-Based View (RBV) and Sociotechnical Systems Theory may be used to anchor future studies on edge computing on real-time data processing. The study recommended expanding theoretical frameworks to include the unique aspects of edge computing, investing in robust edge infrastructure, and developing standardized protocols and best practices. It emphasized the need for government incentives and supportive regulatory frameworks to promote adoption, and suggested that academic institutions incorporate edge computing into curricula. Additionally, the study called for ongoing research to address emerging challenges and opportunities, ensuring continuous advancement and effective implementation of edge computing technologies.

Real-time big data processing for anomaly detection: A Survey

Major advances in telecommunications and the Internet of Things have given rise to numerous smart city scenarios in which smart services are provided. What was once a dream for the future has now become reality. However, the need to provide these smart services quickly, efficiently, in an interoperable manner and in real time is a cutting-edge technological challenge. Although some software architectures offer solutions in this area, these are often limited in terms of reusability and maintenance by independent modules —involving the need for system downtime when maintaining or evolving, as well as by a lack of standards in terms of the interoperability of their interface. In this paper, we propose a fully reusable microservice architecture, standardized through the use of the Web of things paradigm, and with high efficiency in real-time data processing, supported by complex event processing techniques. To illustrate the proposal, we present a fully reusable implementation of the microservices necessary for the deployment of the architecture in the field of air quality monitoring and alerting in smart ports. The performance evaluation of this architecture shows excellent results.

Electronic Health Records: Promises and Realities: A 3-Part Series Part I: The Digital Sea Change, Ready or Not

Among various nanometer-level displacement measurement methods, grating interferometry-based linear encoders are widely used due to their high robustness, relatively low cost, and compactness. One trend of grating encoders is multi-axis measurement capability for simultaneous precision positioning and small order error motion measurement. However, due to both lack of suitable hardware data processing platform and of a real-time displacement calculation system, meeting the requirements of real-time data processing while maintaining the nanometer order resolutions on all these axes is a challenge. To solve above-mentioned problem, in this paper we introduce a design and experimental validation of a field programmable gate array (FPGA)-cored real-time data processing platform for grating encoders. This platform includes the following functions. First, a front-end photodetector and I/V conversion analog circuit are used to realize basic analog signal filtering, while an eight-channel parallel, 16-bit precision, 200 kSPS maximum acquisition rate Analog-to-digital (ADC) is used to obtain digital signals that are easy to process. Then, an FPGA-based digital signal processing platform is implemented, which can calculate the displacement values corresponding to the phase subdivision signals in parallel and in real time at high speed. Finally, the displacement result is transferred by USB2.0 to the PC in real time through an Universal Asynchronous Receiver/Transmitter (UART) serial port to form a complete real-time displacement calculation system. The experimental results show that the system achieves real-time data processing and displacement result display while meeting the high accuracy of traditional offline data solution methods, which demonstrates the industrial potential and practicality of our absolute two-dimensional grating scale displacement measurement system.

In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs.

Map Matching Algorithm for Real-Time Data Processing of Non-route GPS Data in Seoul

Data streaming pipelines have become a revolutionary tool in the field of life sciences, providing novel functionalities for managing and examining the large volumes of data produced in clinical trials. By using real-time data processing, these pipelines improve data integrity and guarantee adherence to regulatory requirements, therefore effectively tackling some of the most crucial obstacles encountered in clinical research. Preserving the integrity and precision of data is of utmost importance in clinical studies. Conventional data management systems sometimes have difficulties in keeping up with the fast flow of data from many sources, resulting in delays, discrepancies, and even problems with compliance. In order to provide continuous, real-time processing of data as it is created, data streaming pipelines offer a solution. This methodology guarantees the prompt validation, cleansing, and processing of data, therefore preserving a consistent degree of precision and minimising the likelihood of mistakes. An inherent advantage of data streaming pipelines is their capacity to manage fast-moving data streams originating from diverse sources, including electronic health records (EHRs), wearable devices, and laboratory equipment. Through the integration of multiple data sources into a cohesive pipeline, researchers may get a holistic perspective of trial results, therefore enabling more informed decision-making and prompt interventions. The real-time characteristic of streaming pipelines also facilitates proactive monitoring, enabling the timely identification of abnormalitie r data quality problems that may affect the results of trials.

Recent advances in BazookaSPECT: Real-time data processing and the development of a gamma-ray microscope

Предложены новые методы повышения эффективности работы алгоритмов обнаружения и детектирования лиц на цифровых изображениях и видеопоследовательностях, основанных на методе Виолы-Джонса, и используемых при решении задач распознавания лиц. Это позволяет устранить влияние одного из главных факторов помех − компенсировать влияние изменений уровня освещенности сцены на качество обнаружения лиц. Для этого в классическую структуру этих алгоритмов дополнительно введена процедура автоматической стабилизации яркости кадра. Описана структура алгоритмов, для них разработано программное обеспечение на языке Python с использованием ресурсов библиотеки OpenCV, что позволяет вести обработку видеоданных в реальном масштабе времени. Предложена и программно реализована оригинальная методика оценки эффективности работы алгоритма по критерию максимума вероятности правильного обнаружения лиц и их главных элементов (глаз, носа, рта). Сопоставляются результаты работы классических и предложенных алгоритмов. Приводятся примеры работы и результаты тестирования программного обеспечения.

With the rapid advancement of global energy storage technologies, surging electricity demand, and the escalating penetration of renewable energy, microgrids have become pivotal in energy supply and resource management, while Electrical Power Grid Systems face growing complexity, diversified fault patterns, and accelerated fault propagation. Conventional methods of data analysis and periodic examination cannot satisfy the immediate requirements of providing quick fault localization, isolation and prevention of further spread which limits the capability of power utilities to recover grid safety and efficiency. The most common algorithms that are being researched include data-driven and intelligent algorithms including Neuromorphic Analytics, Variational Mode Decomposition (VMD) and Convolutional Neural Networks (CNN). These analyses are limited by a lack of flexibility to realistic grid-conditions, cross scenario generalizability, engineering feasibility, and the problem of large scale, accurately labelled data that limits the applications of real-time diagnosis efficacy. To deal with these issues, this paper will present an extensive review and outlook of intelligent real-time feedback based grid fault diagnostics, with perspective of moving away the individual algorithm approaches to an engineering based closed-circuit autonomous technical system. It presents the systematic examination of the data processing in real time (such as the technique of feature extracting and adaptive learning diagnostic model) and evaluates five important system structures, as well as examines their operations on the basis of practical application cases. The research should maximize the efficiency, flexibility, and robustness of grid diagnostic algorithms, provide technical advice on future research, and establish the basis of next generation high reliability and self healed power grid.

The article investigates the problem of detecting traffic objects based on data from video surveillance cameras. The analysis of object detection methods is carried out: from classical approaches using manually created features (SIFT, HOG, LBP) and SVM classifiers, to modern methods based on single-stage convolutional neural networks YOLO and transformers. The aim of the study is to optimize the architecture of the YOLOv8 neural network for recognizing traffic objects, specifically vehicles, from video surveillance cameras. The vehicle class includes cars, buses, and trucks. Particular attention is paid to ensuring high detection quality in difficult conditions, such as night time, adverse weather conditions like rain, fog, or snow, and low image quality. To achieve this, various modifications to the network architecture were explored to balance detection accuracy and computational efficiency, making the model suitable for real-time applications. To evaluate the effectiveness of the proposed approach, a dataset of 1400 images obtained from open sources was created. The images cover different times of day, including dawn, dusk, and nighttime, as well as adverse weather conditions and low-quality images, reflecting real-world scenarios encountered by video surveillance cameras. The dataset was annotated to identify the vehicle class, encompassing cars, buses, and trucks, ensuring robust evaluation under challenging conditions. To optimize the YOLOv8-nano architecture, it was proposed to replace individual C2f blocks with the C3Ghost module in combination with the CBAM attention block. This modification provided a balance between detection quality and computational complexity. As a result of the optimization, the number of parameters was reduced by 26%, and the computational complexity (GFLOPS) was reduced by 10%. At the same time, the detection quality remained comparable to the base model: the average accuracy (mAP@0.50:0.95) decreased slightly from 0.531 to 0.525. The results of the study confirm the promising use of the modified YOLOv8-nano architecture for video surveillance tasks in intelligent transport systems. The proposed approach can be applied in traffic monitoring systems, autonomous vehicle navigation, and other domains requiring fast and accurate video data processing in real time. Future work may focus on further refining the model to handle additional vehicle classes, such as motorcycles and bicycles, to enhance its applicability in diverse scenarios. Additionally, integrating the model into embedded systems for widespread deployment and exploring optimizations for specific camera types or extreme conditions could further improve performance.